Reward-related self-agency is disturbed in depression and anxiety

Background The sense of agency, or the belief in action causality, is an elusive construct that impacts day-to-day experience and decision-making. Despite its relevance in a range of neuropsychiatric disorders, it is widely under-studied and remains difficult to measure objectively in patient populations. We developed and tested a novel cognitive measure of reward-dependent agency perception in an in-person and online cohort. Methods The in-person cohort consisted of 52 healthy control subjects and 20 subjects with depression and anxiety disorders (DA), including major depressive disorder and generalized anxiety disorder. The online sample consisted of 254 participants. The task consisted of an effort implementation for monetary rewards with computerized visual feedback interference and trial-by-trial ratings of self versus other agency. Results All subjects across both cohorts demonstrated higher self-agency after receiving positive-win feedback, compared to negative-loss feedback when the level of computer inference was kept constant. Patients with DA showed reduced positive feedback-dependent agency compared to healthy controls. Finally, in our online sample, we found that higher self-agency following negative-loss feedback was associated with worse anhedonia symptoms. Conclusion Together this work suggests how positive and negative environmental information impacts the sense of self-agency in healthy subjects, and how it is perturbed in patients with depression and anxiety.


Introduction
Subjects were included if they met criteria for MDD or GAD as their primary psychiatric diagnosis as determined by the Structured Clinical Interview for DSM-V Axis Disorders (SCID-V) conducted by a trained rater. Subjects with MDD or GAD, were allowed comorbid mood or anxiety disorders, because of its prevalence [30]. Healthy subjects were free from any current or lifetime psychiatric disorder. For all groups, subjects were excluded if they had an unstable medical illness, history of neurological disease, neurodevelopmental or neurocognitive disorder, or positive urine toxicology test. Subjects completed cognitive testing and self-reported scales on the same day; this included the Temporal Experience of Pleasure Scale (TEPS), which is designed to capture dimensional measure of anhedonia. All methods were performed in accordance with the relevant guidelines and regulations set by the Program for Protection of Human Subjects (PPHS)/Institutional Review Board (IRB) at Icahn School of Medicine at Mount Sinai-approved written informed consent was obtained and subjects were compensated for their time.

Experiment 2: Online participants
A sample of US-based volunteers between the ages of 18-45 were recruited through the Prolific web-based research platform (https://www.prolific.co/). A total of 318 participants were enrolled in the study. Subjects completed an online version of the cognitive task and selfreported scales in the same session. Attention checks were embedded throughout the selfreport questionnaires and any subjects that failed more than one attention check were excluded. Participants were also excluded if cognitive task performance-based data quality thresholds were not met. Performance-based data quality thresholds included performance of at least 10 button presses on average throughout the entire task and an overall average minimum reaction time of 1s, to exclude automatic responses without task engagement. The testing and data collection protocol was approved by the Program for Protection of Human Subjects (PPHS)/Institutional Review Board (IRB) at Icahn School of Medicine at Mount Sinai and subjects were compensated for their time.

The self-agency task (SAT)
The self-agency task was a 20-minute cognitive task programmed in PsychoPy. Throughout the task, subjects performed a simple effort task for reward over 108 trials (Fig 1). Subjects were instructed to press the left and right keyboard buttons quickly to move a visual bar on the computer screen to a target position. During baseline practice trials, reaction times were recorded and used to determine the subsequent threshold (x1.25). Each trial indicated the target bar position and context information on whether the trial was a win trial (for $0.50) or a loss trial (-$0.50). Subjects then performed the button presses to move the bar up to the target position as quickly as possible on each trial. Subjects received outcome feedback based on their performance (win trials: win money or nothing; loss trials: lose money or nothing). Performance was defined as successfully moving the bar to the target position in the available time, i.e. 125% of their average baseline reaction time.
Subjects were instructed that during the task there may be computer interference as they perform the simple effort task. During the task, there were 3 conditions of computer interference to modulate experience of agency, which was not known to the subject. The Self condition includes 18 trials with no computer interference, whereby each pair of button presses (i.e. left + right) moved the bar by 1 position, thus 4 button presses moves the bar up by 2 positions, etc. The Computer condition includes 18 trials with maximum computer interference whereby button presses did not directly equate to incremental bar movement. Here, for each bar position, a randomly determined number of button presses (between 1-6) was required to move the bar by 1 position such that there was a dissociation between button presses and visual feedback of bar movement. The Ambiguous condition included 72 trials with a mix of trials that had mild computer interference (between 1-3 button presses required to move the bar by 1 position). At the end of each trial, subjects rated who was more "in control" during that trial on a 5-point Likert scale where 1 = self, 5 = computer (Fig 1). The majority of trials were Ambiguous as performance during this condition is the main behavioral measure of interest. The Self and Computer condition trials were included firstly as contrast trials to objectively indicate the varying levels of agency and secondly to allow measurement of overall task accuracy and understanding. Subjects undertook training on the task in a self-paced manner before starting the task, including instruction and experience of the maximum computer interference i.e., the "computer" condition. Subjects received bonus monetary payment depending on performance.
The online version of the SAT task was adapted for online testing and was identical to the in-person version. The task was created with PsychoPy3 and hosted on their online platform, Pavlovia (https://pavlovia.org/).

Statistics
In order to determine the impact of outcome on self-agency, agency ratings following positivewin outcomes and negative-loss outcomes (when computer interference was kept constant in Ambiguous trials) were subjected to paired-samples t-tests for all subjects. Second, to examine group differences in self-agency, a two-tailed independent samples t-test was conducted to determine differences in self-agency between HC and DA. Finally, a mixed-effects linear regression was conducted to identify how all variables predicted self-agency ratings, including the effects of outcome feedback (i.e., win/loss), condition (i.e., self/computer/ambiguous), and group (i.e., HC/DA) on agency rating (rating~1 + group + agency + feedback + group:agency + group:feedback + (1 + agency + feedback | subject)). A separate mixed-effects linear regression was also conducted using separate groups of HC, MDD (primary) and GAD (primary) on an exploratory basis. The regression analysis was repeated with the online sample to evaluate how feedback and agency predicted ratings (rating~agency + feedback + (1 + agency | subject) + (1 + feedback | subject). A mixed-effects linear model was used to account for individual differences. The regressions were carried out using the fitlme function in Matlab.

Experiment 1: In person participants
A total of 52 HC subjects performed the task. Two HC subjects did not fully complete the task, leaving 50 HC subjects for analysis (age = 39.4F0B19.9, 21 female). Twenty patients with DA disorders (age = 33.4F0B18.8, 13 female) completed the task including 7 MDD primary, 13 GAD primary, with the majority of DA subjects experiencing significant comorbidity (see Table 1 for demographics).
Self-agency task. All subjects demonstrated higher self-agency after receiving positivewin feedback, compared to negative-loss feedback during the Ambiguous trials when the level  Fig 2). Higher self-agency following the positive-win feedback was associated with higher age across all subjects (R = -0.308, p = 0.006) and for the HC group (R = -0.314, p = 0.045) and the DA group (R = -0.470, p = 0.037), separately (Fig 3). There was no relationship between age and self-agency following negative-loss feedback (p's>0.3), indicating a specificity of this bias in the positive domain. There were no differences within any groups or across groups based on sex (p's>0.5).
Mixed-effects linear regression model. A mixed-effects linear regression was conducted to examine all effects, including group (HC/DA), agency condition (Self, Computer, Ambiguous), and outcome feedback (positive-win, negative-loss) on agency rating.
First, there was a significant effect of agency condition whereby self-agency was higher in the Self group (the reference group in the regression) than the Ambiguous condition (β = 0.40, p < 0.001) or the Computer condition (β = 1.16, p < 0.001), meaning subjects understood the task and performed accurately. Second, there was a significant effect of feedback (β = -0.66, p < 0.001) whereby there was higher self-agency following positive-win compared to negativeloss feedback, corroborating the primary test above. Third, there was no significant main effect of group (β = 0.01, p = 0.97), but there was a significant interaction between group and

PLOS ONE
Self-agency is disturbed in depression and anxiety feedback (β = 0.27, p < 0.05), whereby DA subjects had lower self-agency following positivewin feedback compared to the HC group.
The second exploratory regression model that included separate denotations of MDD-primary and GAD-primary groupings produced largely similar results and suggested that both MDD-primary subjects (β = 0.38, p < 0.05) and GAD-primary subjects (β = 0.23, p = 0.06) had lower self-agency following positive-win feedback compared to HC. See S1 and S2 Tables for all results from the mixed-effects regression models. Dimensional symptom domains. Relationships between self-agency and anhedonia (TEPS) were examined in an exploratory manner. There were no significant relationships between self-agency and dimensional measures of anhedonia in HC and DA groups.

Experiment 2: Online participants
A total of 254 participants performed the task, of which, one participant did not fully complete the task. After the preliminary quality check, seven participants were excluded, leaving a sample size of 246 for analysis (age = 32.83 F0B16.54, 127 female). See Table 2 for subject characteristics.
Self-agency task. Similar to the in-person findings, all subjects demonstrated higher selfagency after receiving positive-win feedback, compared to negative-loss feedback during the Ambiguous trials when the level of computer inference was kept constant (Fig 5A, T(181) = 15.07, p = 8.57x10 -34 ).
Higher self-agency following the positive-win feedback showed a negative relationship with higher age across all subjects, which did not reach significance (R = -0.1, p = 0.135). The correlation was likely not significant due to the restricted age range of this sample (ages 18-45). Again, there was no relationship between age and self-agency following negative-lose feedback, and no differences based on sex (p's>0.5).
Mixed-effects linear regression model. A mixed-effects linear regression was conducted to examine all effects, including agency condition (Self, Computer, Ambiguous), and outcome feedback (positive-win, negative-loss) on agency rating. First, we replicated the significant effect of agency condition (β = 0.39, p = 6.13x10 -54 ) whereby self-agency was higher in the Self group (the reference group in the regression) than the Ambiguous condition (T(490) = -6.59, p = 1.16x10 -10 ) and the Computer condition (T(490) = -11.28, p = 2.24x10 -26 ), meaning subjects understood the task and performed accurately.
Second, there was also a significant effect of feedback (β = 1.10, p = 1.78 x10 -64 ) whereby there was higher self-agency following positive-win compared to negative-loss feedback (T (343) = -14.05, p = 9.82x10 -36 ), corroborating the primary test above. See S3 Table for the results from the mixed-effects regression model. Dimensional symptom domains. Relationships between self-agency and anhedonia were again examined in an exploratory manner across all online subjects. Contrary to the findings in Experiment 1, higher self-agency following negative-loss feedback was associated with worse symptoms of anhedonia (TEPS-anticipatory) in the online sample (R = 0.156, p = 0.036, Fig 5B).

Discussion
This paper showcases an objective study of self-agency in a dynamic environment in healthy individuals and subjects with mood and anxiety disorders. Since self-agency has been associated with syndromes related to anhedonia in depression [24][25][26] and high risk for problematic anxiety [27], this study focused on the symptom domain of anhedonia and its relationship with sense of self-agency in the context of positive and negative feedbacks. The task was also repeated in an online sample, representative of a general population, to capture variations in self-reported anhedonia and relationships with agency. Across all subjects in both experiments, there was higher self-agency in relation to positivewin feedback and lower self-agency in relation to negative-loss feedback. This positive-win feedback-dependent agency generally increased with increasing age. Subjects with mood and anxiety disorders showed lower negative feedback-dependent agency compared to healthy controls in the win trials, with no difference during the loss trials. Higher self-agency in response to negative-loss feedback was also associated with worse self-reported anhedonia symptoms in our online participants. Together, these results indicate a novel belief bias that presents across all subjects and suggests a potential trans-diagnostic disturbance related to reduction in self-agency.
The current results corroborate literature on self-agency bias in healthy subjects that increases with age [17]. However, this extends upon previous work by utilizing an objective cognitive task that directly separates self-agency in response to positive feedbacks compared to negative feedbacks, highlighting that only the positive feedback-dependent agency increases with age.
Reduced self-agency reporting in response to negative feedback in subjects with DA disorders was expected given previous literature linking self-reported self-agency and self-reported depression [28] and anxiety [17] symptoms. These results were also replicated in our online sample. However, the current findings suggest that in this sample of patients, reduced self-bias or a more external locus of control exists more so in a positive environment only, rather than in a negative environment. This is surprising given a wealth of literature highlighting negative biases in depression and anxiety disorders. Selective memory recall for negative information [17] and negative bias related to perception of facial expressions [31] is higher in depressed cohorts. Similarly, attentional bias towards negative information [32] and negative interpretation bias of ambiguous information [33] is well established in anxiety disorders. However, while negative attentional bias is inconsistently reported in depression, this bias seems to increase for self-relevant negative information [34,35], whereby depressed subjects relate negative information to self more than control subjects. This suggests that negative self-biases in depression may relate to negative self-evaluation, rather than attributing negative environmental outcomes to ones' actions. While there were no group differences in negative self-bias in these samples, higher negative self-bias was associated with anhedonia in a larger populationbased cohort. A recent study also found that arousal levels might selectively modulate the selfrelevant processes in depressive mood [36]. This role of arousal as a possible confound may also explain differences in the results.
Together, these results suggest an interesting avenue for exploring possible relationships between anhedonia symptoms related to lower reward-dependent agency in response to negative outcomes. However, these findings must be replicated in another study before confirming specific links between anhedonia and reward-dependent self-agency in response to negative feedback.

Limitations and future directions
Finally, while mood and anxiety disorders and symptoms are highly comorbid [30,37], further studies in larger sample sizes must disentangle the relative contributions of reward-dependent self-agency to depression, anhedonia, stress and anxiety-related symptoms across and within disorder groups. Moving away from categorical diagnoses of disorders, it will be important to dissect how specific symptom domains, such as rumination or threat-reactivity relate to underlying disturbances in self-agency across groups. If these findings are replicated, an important future avenue of research would be to examine how agency belief-updating can be modulated using self-agency related feedback or training, in order to normalize aberrant self-agency biases related to symptoms across patient groups.
Supporting information S1 Table. Results of the linear mixed effects model to test agency and feedback as predictors of rating (sense of agency) for patients with depression and anxiety disorder (DA). (DOCX) S2 Table. Results of the linear mixed effects model to test agency and feedback as predictors of rating (sense of agency) for patients with primary generalized anxiety disorder (GAD) and major depressive disorder (MDD), which compromised the depression and anxiety disorder (DA) group. (DOCX) S3 Table. Results of the linear mixed effects model to test agency (self, ambiguous and computer) and feedback (positive-win and negative-loss) as predictors of rating (sense of agency) for the online sample.